Due to massive information overload on the web it's hard to index and reuse existing learning resources. Classifying learning resources according to domain specific concept hierarchies could address the problem of indexing and reusability. Manual classification is a tedious task and, as a result, automatic classifiers are in high demand. For this task we present an automated approach based on machine learning technique to exploit hierarchal knowledge in order to classify learning resources in a given hierarchy of concepts. We show by experimentation that using hierarchical information and content of unclassified documents provides better accuracy.

Due to massive information overload on the web it's hard to index and reuse existing learning resources. Classifying learning resources according to domain specific concept hierarchies could address the problem of indexing and reusability. Manual classification is a tedious task and, as a result, automatic classifiers are in high demand. For this task we present an automated approach based on machine learning technique to exploit hierarchal knowledge in order to classify learning resources in a given hierarchy of concepts. We show by experimentation that using hierarchical information and content of unclassified documents provides better accuracy.